Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations136
Missing cells624
Missing cells (%)21.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory48.0 KiB
Average record size in memory361.3 B

Variable types

Text1
Numeric13
Categorical4
Unsupported3

Alerts

Year has constant value "2018" Constant
Electric power consumption (kWh per capita) has constant value "0.0" Constant
Freedom to make life choices is highly overall correlated with ScoreHigh correlation
GDP (USD) is highly overall correlated with GDP per capita and 4 other fieldsHigh correlation
GDP per capita is highly overall correlated with GDP (USD) and 7 other fieldsHigh correlation
GDP per capita (USD) is highly overall correlated with GDP (USD) and 6 other fieldsHigh correlation
Healthy life expectancy is highly overall correlated with GDP (USD) and 7 other fieldsHigh correlation
IncomeGroup is highly overall correlated with GDP per capita and 2 other fieldsHigh correlation
Individuals using the Internet (% of population) is highly overall correlated with GDP per capita and 8 other fieldsHigh correlation
Infant mortality rate (per 1,000 live births) is highly overall correlated with GDP (USD) and 6 other fieldsHigh correlation
Perceptions of corruption is highly overall correlated with Individuals using the Internet (% of population)High correlation
Region is highly overall correlated with Individuals using the Internet (% of population)High correlation
Score is highly overall correlated with Freedom to make life choices and 7 other fieldsHigh correlation
Social support is highly overall correlated with GDP per capita and 5 other fieldsHigh correlation
Birth rate, crude (per 1,000 people) has 136 (100.0%) missing values Missing
Death rate, crude (per 1,000 people) has 136 (100.0%) missing values Missing
Electric power consumption (kWh per capita) has 135 (99.3%) missing values Missing
Individuals using the Internet (% of population) has 76 (55.9%) missing values Missing
Life expectancy at birth (years) has 136 (100.0%) missing values Missing
Population density (people per sq. km of land area) has 2 (1.5%) missing values Missing
Country Name has unique values Unique
Unemployment (% of total labor force) (modeled ILO estimate) has unique values Unique
Birth rate, crude (per 1,000 people) is an unsupported type, check if it needs cleaning or further analysis Unsupported
Death rate, crude (per 1,000 people) is an unsupported type, check if it needs cleaning or further analysis Unsupported
Life expectancy at birth (years) is an unsupported type, check if it needs cleaning or further analysis Unsupported
Perceptions of corruption has 2 (1.5%) zeros Zeros

Reproduction

Analysis started2025-03-12 18:37:41.960239
Analysis finished2025-03-12 18:37:50.527200
Duration8.57 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Country Name
Text

Unique 

Distinct136
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size8.7 KiB
2025-03-12T20:37:50.639395image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length24
Median length20
Mean length7.9044118
Min length4

Characters and Unicode

Total characters1075
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique136 ?
Unique (%)100.0%

Sample

1st rowAfghanistan
2nd rowAlbania
3rd rowAlgeria
4th rowAngola
5th rowArgentina
ValueCountFrequency (%)
republic 3
 
1.9%
united 3
 
1.9%
sudan 2
 
1.3%
south 2
 
1.3%
afghanistan 1
 
0.6%
bolivia 1
 
0.6%
cyprus 1
 
0.6%
bhutan 1
 
0.6%
benin 1
 
0.6%
belize 1
 
0.6%
Other values (139) 139
89.7%
2025-03-12T20:37:50.828399image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 177
16.5%
i 99
 
9.2%
n 87
 
8.1%
e 72
 
6.7%
r 62
 
5.8%
o 51
 
4.7%
l 42
 
3.9%
t 40
 
3.7%
u 40
 
3.7%
d 32
 
3.0%
Other values (39) 373
34.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1075
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 177
16.5%
i 99
 
9.2%
n 87
 
8.1%
e 72
 
6.7%
r 62
 
5.8%
o 51
 
4.7%
l 42
 
3.9%
t 40
 
3.7%
u 40
 
3.7%
d 32
 
3.0%
Other values (39) 373
34.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1075
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 177
16.5%
i 99
 
9.2%
n 87
 
8.1%
e 72
 
6.7%
r 62
 
5.8%
o 51
 
4.7%
l 42
 
3.9%
t 40
 
3.7%
u 40
 
3.7%
d 32
 
3.0%
Other values (39) 373
34.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1075
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 177
16.5%
i 99
 
9.2%
n 87
 
8.1%
e 72
 
6.7%
r 62
 
5.8%
o 51
 
4.7%
l 42
 
3.9%
t 40
 
3.7%
u 40
 
3.7%
d 32
 
3.0%
Other values (39) 373
34.7%

Score
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3382353
Minimum3
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2025-03-12T20:37:50.886234image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4
Q14
median5
Q36
95-th percentile7
Maximum8
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.175179
Coefficient of variation (CV)0.22014373
Kurtosis-0.6879599
Mean5.3382353
Median Absolute Deviation (MAD)1
Skewness0.0072007625
Sum726
Variance1.3810458
MonotonicityNot monotonic
2025-03-12T20:37:50.931542image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
6 43
31.6%
5 33
24.3%
4 32
23.5%
7 19
14.0%
3 6
 
4.4%
8 3
 
2.2%
ValueCountFrequency (%)
3 6
 
4.4%
4 32
23.5%
5 33
24.3%
6 43
31.6%
7 19
14.0%
8 3
 
2.2%
ValueCountFrequency (%)
8 3
 
2.2%
7 19
14.0%
6 43
31.6%
5 33
24.3%
4 32
23.5%
3 6
 
4.4%

GDP per capita
Real number (ℝ)

High correlation 

Distinct130
Distinct (%)95.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.89079412
Minimum0
Maximum2.096
Zeros1
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2025-03-12T20:37:50.987435image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2415
Q10.60175
median0.95
Q31.19025
95-th percentile1.427
Maximum2.096
Range2.096
Interquartile range (IQR)0.5885

Descriptive statistics

Standard deviation0.39845978
Coefficient of variation (CV)0.4473085
Kurtosis-0.34617233
Mean0.89079412
Median Absolute Deviation (MAD)0.2825
Skewness-0.18682048
Sum121.148
Variance0.15877019
MonotonicityNot monotonic
2025-03-12T20:37:51.051221image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.332 2
 
1.5%
1.01 2
 
1.5%
1.016 2
 
1.5%
1.34 2
 
1.5%
1.017 2
 
1.5%
1.148 2
 
1.5%
0.835 1
 
0.7%
1.112 1
 
0.7%
0.652 1
 
0.7%
0.682 1
 
0.7%
Other values (120) 120
88.2%
ValueCountFrequency (%)
0 1
0.7%
0.024 1
0.7%
0.076 1
0.7%
0.091 1
0.7%
0.131 1
0.7%
0.186 1
0.7%
0.198 1
0.7%
0.256 1
0.7%
0.259 1
0.7%
0.262 1
0.7%
ValueCountFrequency (%)
2.096 1
0.7%
1.649 1
0.7%
1.576 1
0.7%
1.529 1
0.7%
1.474 1
0.7%
1.456 1
0.7%
1.448 1
0.7%
1.42 1
0.7%
1.398 1
0.7%
1.379 1
0.7%

Social support
Real number (ℝ)

High correlation 

Distinct126
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2201544
Minimum0
Maximum1.644
Zeros1
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2025-03-12T20:37:51.114374image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.62225
Q11.0855
median1.2735
Q31.4665
95-th percentile1.57525
Maximum1.644
Range1.644
Interquartile range (IQR)0.381

Descriptive statistics

Standard deviation0.30417755
Coefficient of variation (CV)0.24929431
Kurtosis1.270942
Mean1.2201544
Median Absolute Deviation (MAD)0.1935
Skewness-1.1126255
Sum165.941
Variance0.092523983
MonotonicityNot monotonic
2025-03-12T20:37:51.178898image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.174 2
 
1.5%
1.501 2
 
1.5%
1.474 2
 
1.5%
1.215 2
 
1.5%
1.301 2
 
1.5%
1.459 2
 
1.5%
1.161 2
 
1.5%
0.896 2
 
1.5%
1.532 2
 
1.5%
1.331 2
 
1.5%
Other values (116) 116
85.3%
ValueCountFrequency (%)
0 1
0.7%
0.372 1
0.7%
0.474 1
0.7%
0.537 1
0.7%
0.541 1
0.7%
0.592 1
0.7%
0.608 1
0.7%
0.627 1
0.7%
0.712 1
0.7%
0.714 1
0.7%
ValueCountFrequency (%)
1.644 1
0.7%
1.601 1
0.7%
1.592 1
0.7%
1.59 1
0.7%
1.584 1
0.7%
1.583 1
0.7%
1.582 1
0.7%
1.573 1
0.7%
1.559 1
0.7%
1.549 1
0.7%

Healthy life expectancy
Real number (ℝ)

High correlation 

Distinct128
Distinct (%)94.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.59691176
Minimum0
Maximum1.008
Zeros1
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2025-03-12T20:37:51.245014image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.15025
Q10.41375
median0.6675
Q30.782
95-th percentile0.91075
Maximum1.008
Range1.008
Interquartile range (IQR)0.36825

Descriptive statistics

Standard deviation0.24922067
Coefficient of variation (CV)0.41751676
Kurtosis-0.61305617
Mean0.59691176
Median Absolute Deviation (MAD)0.1875
Skewness-0.56145814
Sum81.18
Variance0.06211094
MonotonicityNot monotonic
2025-03-12T20:37:51.311086image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.675 2
 
1.5%
0.876 2
 
1.5%
0.737 2
 
1.5%
0.669 2
 
1.5%
0.884 2
 
1.5%
0.896 2
 
1.5%
0.861 2
 
1.5%
0.7 2
 
1.5%
0.878 1
 
0.7%
0.221 1
 
0.7%
Other values (118) 118
86.8%
ValueCountFrequency (%)
0 1
0.7%
0.01 1
0.7%
0.048 1
0.7%
0.053 1
0.7%
0.079 1
0.7%
0.115 1
0.7%
0.145 1
0.7%
0.152 1
0.7%
0.173 1
0.7%
0.177 1
0.7%
ValueCountFrequency (%)
1.008 1
0.7%
0.988 1
0.7%
0.965 1
0.7%
0.946 1
0.7%
0.927 1
0.7%
0.914 1
0.7%
0.913 1
0.7%
0.91 1
0.7%
0.909 1
0.7%
0.908 1
0.7%

Freedom to make life choices
Real number (ℝ)

High correlation 

Distinct122
Distinct (%)89.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.46390441
Minimum0
Maximum0.724
Zeros1
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2025-03-12T20:37:51.372291image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.12475
Q10.366
median0.503
Q30.58425
95-th percentile0.67025
Maximum0.724
Range0.724
Interquartile range (IQR)0.21825

Descriptive statistics

Standard deviation0.16318664
Coefficient of variation (CV)0.35176781
Kurtosis0.31924634
Mean0.46390441
Median Absolute Deviation (MAD)0.099
Skewness-0.88449224
Sum63.091
Variance0.02662988
MonotonicityNot monotonic
2025-03-12T20:37:51.434781image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.531 3
 
2.2%
0.423 2
 
1.5%
0.632 2
 
1.5%
0.454 2
 
1.5%
0.259 2
 
1.5%
0.541 2
 
1.5%
0.356 2
 
1.5%
0.597 2
 
1.5%
0.58 2
 
1.5%
0.419 2
 
1.5%
Other values (112) 115
84.6%
ValueCountFrequency (%)
0 1
0.7%
0.016 1
0.7%
0.025 1
0.7%
0.065 1
0.7%
0.077 1
0.7%
0.085 1
0.7%
0.112 1
0.7%
0.129 1
0.7%
0.131 1
0.7%
0.163 1
0.7%
ValueCountFrequency (%)
0.724 1
0.7%
0.696 1
0.7%
0.686 1
0.7%
0.683 1
0.7%
0.681 1
0.7%
0.677 1
0.7%
0.674 1
0.7%
0.669 1
0.7%
0.66 1
0.7%
0.659 1
0.7%

Generosity
Real number (ℝ)

Distinct110
Distinct (%)80.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.18209559
Minimum0
Maximum0.598
Zeros1
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2025-03-12T20:37:51.497284image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0395
Q10.11075
median0.175
Q30.239
95-th percentile0.36175
Maximum0.598
Range0.598
Interquartile range (IQR)0.12825

Descriptive statistics

Standard deviation0.10033284
Coefficient of variation (CV)0.55098998
Kurtosis1.5169138
Mean0.18209559
Median Absolute Deviation (MAD)0.0645
Skewness0.883607
Sum24.765
Variance0.01006668
MonotonicityNot monotonic
2025-03-12T20:37:51.560854image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.256 3
 
2.2%
0.12 3
 
2.2%
0.134 3
 
2.2%
0.13 2
 
1.5%
0.148 2
 
1.5%
0.175 2
 
1.5%
0.197 2
 
1.5%
0.202 2
 
1.5%
0.098 2
 
1.5%
0.307 2
 
1.5%
Other values (100) 113
83.1%
ValueCountFrequency (%)
0 1
0.7%
0.026 2
1.5%
0.029 1
0.7%
0.031 1
0.7%
0.032 1
0.7%
0.038 1
0.7%
0.04 1
0.7%
0.042 1
0.7%
0.051 1
0.7%
0.055 2
1.5%
ValueCountFrequency (%)
0.598 1
0.7%
0.484 1
0.7%
0.392 1
0.7%
0.376 1
0.7%
0.365 1
0.7%
0.364 2
1.5%
0.361 1
0.7%
0.354 1
0.7%
0.353 1
0.7%
0.352 1
0.7%

Perceptions of corruption
Real number (ℝ)

High correlation  Zeros 

Distinct103
Distinct (%)76.3%
Missing1
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean0.11585185
Minimum0
Maximum0.457
Zeros2
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2025-03-12T20:37:51.760454image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0177
Q10.0515
median0.082
Q30.14
95-th percentile0.3451
Maximum0.457
Range0.457
Interquartile range (IQR)0.0885

Descriptive statistics

Standard deviation0.099723387
Coefficient of variation (CV)0.86078372
Kurtosis2.1901115
Mean0.11585185
Median Absolute Deviation (MAD)0.043
Skewness1.6237132
Sum15.64
Variance0.009944754
MonotonicityNot monotonic
2025-03-12T20:37:51.824892image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.082 5
 
3.7%
0.074 4
 
2.9%
0.032 3
 
2.2%
0.061 3
 
2.2%
0.106 3
 
2.2%
0.034 3
 
2.2%
0.171 2
 
1.5%
0.043 2
 
1.5%
0.039 2
 
1.5%
0.05 2
 
1.5%
Other values (93) 106
77.9%
ValueCountFrequency (%)
0 2
1.5%
0.001 1
0.7%
0.006 1
0.7%
0.009 1
0.7%
0.011 1
0.7%
0.017 1
0.7%
0.018 1
0.7%
0.022 1
0.7%
0.028 2
1.5%
0.029 2
1.5%
ValueCountFrequency (%)
0.457 1
0.7%
0.444 1
0.7%
0.408 1
0.7%
0.393 1
0.7%
0.389 1
0.7%
0.383 1
0.7%
0.357 1
0.7%
0.34 1
0.7%
0.321 1
0.7%
0.306 1
0.7%

Region
Categorical

High correlation 

Distinct7
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Memory size10.4 KiB
Europe & Central Asia
44 
Sub-Saharan Africa
36 
Latin America & Caribbean
20 
Middle East & North Africa
14 
East Asia & Pacific
13 
Other values (2)

Length

Max length26
Median length25
Mean length20.433824
Min length10

Characters and Unicode

Total characters2779
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSouth Asia
2nd rowEurope & Central Asia
3rd rowMiddle East & North Africa
4th rowSub-Saharan Africa
5th rowLatin America & Caribbean

Common Values

ValueCountFrequency (%)
Europe & Central Asia 44
32.4%
Sub-Saharan Africa 36
26.5%
Latin America & Caribbean 20
14.7%
Middle East & North Africa 14
 
10.3%
East Asia & Pacific 13
 
9.6%
South Asia 7
 
5.1%
North America 2
 
1.5%

Length

2025-03-12T20:37:51.889992image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-12T20:37:51.950456image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
91
19.4%
asia 64
13.7%
africa 50
10.7%
europe 44
9.4%
central 44
9.4%
sub-saharan 36
 
7.7%
east 27
 
5.8%
america 22
 
4.7%
latin 20
 
4.3%
caribbean 20
 
4.3%
Other values (4) 50
10.7%

Most occurring characters

ValueCountFrequency (%)
a 388
14.0%
332
 
11.9%
r 232
 
8.3%
i 216
 
7.8%
e 144
 
5.2%
A 136
 
4.9%
n 120
 
4.3%
t 114
 
4.1%
c 98
 
3.5%
s 91
 
3.3%
Other values (18) 908
32.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2779
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 388
14.0%
332
 
11.9%
r 232
 
8.3%
i 216
 
7.8%
e 144
 
5.2%
A 136
 
4.9%
n 120
 
4.3%
t 114
 
4.1%
c 98
 
3.5%
s 91
 
3.3%
Other values (18) 908
32.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2779
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 388
14.0%
332
 
11.9%
r 232
 
8.3%
i 216
 
7.8%
e 144
 
5.2%
A 136
 
4.9%
n 120
 
4.3%
t 114
 
4.1%
c 98
 
3.5%
s 91
 
3.3%
Other values (18) 908
32.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2779
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 388
14.0%
332
 
11.9%
r 232
 
8.3%
i 216
 
7.8%
e 144
 
5.2%
A 136
 
4.9%
n 120
 
4.3%
t 114
 
4.1%
c 98
 
3.5%
s 91
 
3.3%
Other values (18) 908
32.7%

IncomeGroup
Categorical

High correlation 

Distinct5
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size10.0 KiB
Upper middle income
38 
Lower middle income
30 
High income: OECD
30 
Low income
25 
High income: nonOECD
13 

Length

Max length20
Median length19.5
Mean length17
Min length10

Characters and Unicode

Total characters2312
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLow income
2nd rowUpper middle income
3rd rowUpper middle income
4th rowUpper middle income
5th rowHigh income: nonOECD

Common Values

ValueCountFrequency (%)
Upper middle income 38
27.9%
Lower middle income 30
22.1%
High income: OECD 30
22.1%
Low income 25
18.4%
High income: nonOECD 13
 
9.6%

Length

2025-03-12T20:37:52.013559image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-12T20:37:52.062869image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
income 136
35.5%
middle 68
17.8%
high 43
 
11.2%
upper 38
 
9.9%
lower 30
 
7.8%
oecd 30
 
7.8%
low 25
 
6.5%
nonoecd 13
 
3.4%

Most occurring characters

ValueCountFrequency (%)
e 272
11.8%
247
10.7%
i 247
10.7%
o 204
 
8.8%
m 204
 
8.8%
n 162
 
7.0%
d 136
 
5.9%
c 136
 
5.9%
p 76
 
3.3%
l 68
 
2.9%
Other values (12) 560
24.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2312
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 272
11.8%
247
10.7%
i 247
10.7%
o 204
 
8.8%
m 204
 
8.8%
n 162
 
7.0%
d 136
 
5.9%
c 136
 
5.9%
p 76
 
3.3%
l 68
 
2.9%
Other values (12) 560
24.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2312
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 272
11.8%
247
10.7%
i 247
10.7%
o 204
 
8.8%
m 204
 
8.8%
n 162
 
7.0%
d 136
 
5.9%
c 136
 
5.9%
p 76
 
3.3%
l 68
 
2.9%
Other values (12) 560
24.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2312
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 272
11.8%
247
10.7%
i 247
10.7%
o 204
 
8.8%
m 204
 
8.8%
n 162
 
7.0%
d 136
 
5.9%
c 136
 
5.9%
p 76
 
3.3%
l 68
 
2.9%
Other values (12) 560
24.2%

Year
Categorical

Constant 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size8.2 KiB
2018
136 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters544
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018
2nd row2018
3rd row2018
4th row2018
5th row2018

Common Values

ValueCountFrequency (%)
2018 136
100.0%

Length

2025-03-12T20:37:52.120806image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-12T20:37:52.163836image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
2018 136
100.0%

Most occurring characters

ValueCountFrequency (%)
2 136
25.0%
0 136
25.0%
1 136
25.0%
8 136
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 544
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 136
25.0%
0 136
25.0%
1 136
25.0%
8 136
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 544
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 136
25.0%
0 136
25.0%
1 136
25.0%
8 136
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 544
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 136
25.0%
0 136
25.0%
1 136
25.0%
8 136
25.0%

Birth rate, crude (per 1,000 people)
Unsupported

Missing  Rejected  Unsupported 

Missing136
Missing (%)100.0%
Memory size1.2 KiB

Death rate, crude (per 1,000 people)
Unsupported

Missing  Rejected  Unsupported 

Missing136
Missing (%)100.0%
Memory size1.2 KiB

Electric power consumption (kWh per capita)
Categorical

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing135
Missing (%)99.3%
Memory size8.6 KiB
0.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row0.0

Common Values

ValueCountFrequency (%)
0.0 1
 
0.7%
(Missing) 135
99.3%

Length

2025-03-12T20:37:52.207782image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-12T20:37:52.248012image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
0 2
66.7%
. 1
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2
66.7%
. 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2
66.7%
. 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2
66.7%
. 1
33.3%

GDP (USD)
Real number (ℝ)

High correlation 

Distinct132
Distinct (%)97.8%
Missing1
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean5.8778631 × 1011
Minimum1.925 × 109
Maximum2.05 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2025-03-12T20:37:52.297272image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1.925 × 109
5-th percentile4.504685 × 109
Q11.790635 × 1010
median6.0126 × 1010
Q33.305 × 1011
95-th percentile2.268 × 1012
Maximum2.05 × 1013
Range2.0498075 × 1013
Interquartile range (IQR)3.1259365 × 1011

Descriptive statistics

Standard deviation2.1967361 × 1012
Coefficient of variation (CV)3.737304
Kurtosis59.425882
Mean5.8778631 × 1011
Median Absolute Deviation (MAD)5.088649 × 1010
Skewness7.3222605
Sum7.9351151 × 1013
Variance4.8256496 × 1024
MonotonicityNot monotonic
2025-03-12T20:37:52.362341image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.45 × 10112
 
1.5%
2.74 × 10112
 
1.5%
1.43 × 10122
 
1.5%
2.88125 × 10101
 
0.7%
9.14 × 10111
 
0.7%
2.05 × 10111
 
0.7%
1.31179 × 10101
 
0.7%
9239510000 1
 
0.7%
3.97 × 10111
 
0.7%
4.35 × 10111
 
0.7%
Other values (122) 122
89.7%
ValueCountFrequency (%)
1925000000 1
0.7%
2379720000 1
0.7%
2534970000 1
0.7%
2791760000 1
0.7%
3078030000 1
0.7%
3249000000 1
0.7%
3999950000 1
0.7%
4721000000 1
0.7%
5300210000 1
0.7%
5365870000 1
0.7%
ValueCountFrequency (%)
2.05 × 10131
0.7%
1.36 × 10131
0.7%
4.97 × 10121
0.7%
4 × 10121
0.7%
2.83 × 10121
0.7%
2.78 × 10121
0.7%
2.73 × 10121
0.7%
2.07 × 10121
0.7%
1.87 × 10121
0.7%
1.71 × 10121
0.7%

GDP per capita (USD)
Real number (ℝ)

High correlation 

Distinct135
Distinct (%)100.0%
Missing1
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean16076.07
Minimum275.43
Maximum114340
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2025-03-12T20:37:52.422019image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum275.43
5-th percentile517.6192
Q11704.385
median6289.94
Q321626
95-th percentile63223.27
Maximum114340
Range114064.57
Interquartile range (IQR)19921.615

Descriptive statistics

Standard deviation21948.862
Coefficient of variation (CV)1.3653126
Kurtosis3.5920621
Mean16076.07
Median Absolute Deviation (MAD)5312.666
Skewness1.9290367
Sum2170269.5
Variance4.8175253 × 108
MonotonicityNot monotonic
2025-03-12T20:37:52.482429image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
520.897 1
 
0.7%
2028.18 1
 
0.7%
5931.45 1
 
0.7%
1025.8 1
 
0.7%
53024.1 1
 
0.7%
41966 1
 
0.7%
2028.9 1
 
0.7%
411.689 1
 
0.7%
81807.2 1
 
0.7%
5253.63 1
 
0.7%
Other values (125) 125
91.9%
ValueCountFrequency (%)
275.43 1
0.7%
314.562 1
0.7%
389.398 1
0.7%
411.689 1
0.7%
460.753 1
0.7%
490.168 1
0.7%
509.971 1
0.7%
520.897 1
0.7%
522.858 1
0.7%
643.14 1
0.7%
ValueCountFrequency (%)
114340 1
0.7%
82838.9 1
0.7%
81807.2 1
0.7%
78806.4 1
0.7%
73201.7 1
0.7%
69026.5 1
0.7%
64581.9 1
0.7%
62641 1
0.7%
60726.5 1
0.7%
57305.3 1
0.7%

Individuals using the Internet (% of population)
Real number (ℝ)

High correlation  Missing 

Distinct60
Distinct (%)100.0%
Missing76
Missing (%)55.9%
Infinite0
Infinite (%)0.0%
Mean75.459338
Minimum5.25049
Maximum99.6528
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2025-03-12T20:37:52.544869image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum5.25049
5-th percentile39.421805
Q169.033225
median79.0169
Q388.713825
95-th percentile98.66178
Maximum99.6528
Range94.40231
Interquartile range (IQR)19.6806

Descriptive statistics

Standard deviation19.505123
Coefficient of variation (CV)0.25848521
Kurtosis3.1192389
Mean75.459338
Median Absolute Deviation (MAD)9.75565
Skewness-1.5121538
Sum4527.5603
Variance380.44982
MonotonicityNot monotonic
2025-03-12T20:37:52.611594image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
79.7226 1
 
0.7%
81.2017 1
 
0.7%
81.4031 1
 
0.7%
58.5962 1
 
0.7%
65.7726 1
 
0.7%
71.5173 1
 
0.7%
64.8039 1
 
0.7%
94.7121 1
 
0.7%
5.25049 1
 
0.7%
96.4917 1
 
0.7%
Other values (50) 50
36.8%
(Missing) 76
55.9%
ValueCountFrequency (%)
5.25049 1
0.7%
14.3 1
0.7%
32.4736 1
0.7%
39.7875 1
0.7%
40 1
0.7%
52.5403 1
0.7%
56.8175 1
0.7%
58.5962 1
0.7%
59.5797 1
0.7%
63.9686 1
0.7%
ValueCountFrequency (%)
99.6528 1
0.7%
99.6 1
0.7%
99.011 1
0.7%
98.6434 1
0.7%
98.45 1
0.7%
97.6443 1
0.7%
97.0613 1
0.7%
96.4917 1
0.7%
94.8967 1
0.7%
94.7121 1
0.7%

Infant mortality rate (per 1,000 live births)
Real number (ℝ)

High correlation 

Distinct111
Distinct (%)81.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.828676
Minimum1.4
Maximum84.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2025-03-12T20:37:52.673860image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1.4
5-th percentile2.1
Q14.6
median12.3
Q332.025
95-th percentile64
Maximum84.5
Range83.1
Interquartile range (IQR)27.425

Descriptive statistics

Standard deviation20.557576
Coefficient of variation (CV)0.98698425
Kurtosis0.61134722
Mean20.828676
Median Absolute Deviation (MAD)9.35
Skewness1.2106147
Sum2832.7
Variance422.61391
MonotonicityNot monotonic
2025-03-12T20:37:52.733867image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.1 4
 
2.9%
3.6 4
 
2.9%
3.3 3
 
2.2%
6.1 3
 
2.2%
6.4 3
 
2.2%
11 2
 
1.5%
8.8 2
 
1.5%
2.9 2
 
1.5%
19.2 2
 
1.5%
13.6 2
 
1.5%
Other values (101) 109
80.1%
ValueCountFrequency (%)
1.4 1
0.7%
1.5 1
0.7%
1.7 1
0.7%
1.8 1
0.7%
1.9 2
1.5%
2.1 2
1.5%
2.2 1
0.7%
2.3 2
1.5%
2.5 1
0.7%
2.6 2
1.5%
ValueCountFrequency (%)
84.5 1
0.7%
78.5 1
0.7%
76.6 1
0.7%
75.7 1
0.7%
71.4 1
0.7%
65.7 1
0.7%
64.9 1
0.7%
63.7 1
0.7%
62 1
0.7%
60.5 1
0.7%

Life expectancy at birth (years)
Unsupported

Missing  Rejected  Unsupported 

Missing136
Missing (%)100.0%
Memory size1.2 KiB
Distinct134
Distinct (%)100.0%
Missing2
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean212.14414
Minimum2.04061
Maximum7953
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2025-03-12T20:37:52.793986image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum2.04061
5-th percentile4.2032625
Q130.5354
median82.29305
Q3148.19975
95-th percentile503.1393
Maximum7953
Range7950.9594
Interquartile range (IQR)117.66435

Descriptive statistics

Standard deviation721.52951
Coefficient of variation (CV)3.4011287
Kurtosis101.56249
Mean212.14414
Median Absolute Deviation (MAD)57.2617
Skewness9.6080486
Sum28427.315
Variance520604.83
MonotonicityNot monotonic
2025-03-12T20:37:52.858590image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56.9378 1
 
0.7%
17.7177 1
 
0.7%
2.97375 1
 
0.7%
195.939 1
 
0.7%
511.458 1
 
0.7%
18.5542 1
 
0.7%
53.727 1
 
0.7%
215.065 1
 
0.7%
112.239 1
 
0.7%
14.5549 1
 
0.7%
Other values (124) 124
91.2%
(Missing) 2
 
1.5%
ValueCountFrequency (%)
2.04061 1
0.7%
2.97375 1
0.7%
3.24913 1
0.7%
3.52692 1
0.7%
3.79563 1
0.7%
3.97742 1
0.7%
4.07531 1
0.7%
4.27216 1
0.7%
6.76983 1
0.7%
7.49041 1
0.7%
ValueCountFrequency (%)
7953 1
0.7%
2017.27 1
0.7%
1511.03 1
0.7%
1239.58 1
0.7%
669.494 1
0.7%
623.302 1
0.7%
511.458 1
0.7%
498.66 1
0.7%
454.938 1
0.7%
435.178 1
0.7%
Distinct136
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.7790368
Minimum0.142
Maximum26.958
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2025-03-12T20:37:52.924926image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.142
5-th percentile1.462
Q13.36075
median5.242
Q39.05325
95-th percentile17.76925
Maximum26.958
Range26.816
Interquartile range (IQR)5.6925

Descriptive statistics

Standard deviation5.2171841
Coefficient of variation (CV)0.76960552
Kurtosis2.2660297
Mean6.7790368
Median Absolute Deviation (MAD)2.514
Skewness1.518802
Sum921.949
Variance27.21901
MonotonicityNot monotonic
2025-03-12T20:37:52.988438image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.542 1
 
0.7%
6.026 1
 
0.7%
23.095 1
 
0.7%
1.264 1
 
0.7%
3.88 1
 
0.7%
4.522 1
 
0.7%
4.479 1
 
0.7%
0.273 1
 
0.7%
3.919 1
 
0.7%
13.898 1
 
0.7%
Other values (126) 126
92.6%
ValueCountFrequency (%)
0.142 1
0.7%
0.273 1
0.7%
0.665 1
0.7%
0.962 1
0.7%
0.971 1
0.7%
1.048 1
0.7%
1.264 1
0.7%
1.528 1
0.7%
1.542 1
0.7%
1.564 1
0.7%
ValueCountFrequency (%)
26.958 1
0.7%
23.596 1
0.7%
23.095 1
0.7%
20.839 1
0.7%
19.488 1
0.7%
19.207 1
0.7%
17.941 1
0.7%
17.712 1
0.7%
17.287 1
0.7%
15.487 1
0.7%

Interactions

2025-03-12T20:37:49.505618image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:42.135601image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:42.892370image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:43.425216image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:43.984416image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:44.733558image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:45.315429image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:46.009139image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:46.669120image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:47.203972image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:47.739988image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:48.280903image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:48.814959image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:49.688254image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:42.217883image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:42.932224image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:43.468075image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:44.060811image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:44.775196image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:45.352930image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:46.048153image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:46.710766image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:47.245563image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:47.783505image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:48.319304image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:48.859499image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:49.727300image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:42.260115image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:42.970161image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:43.507611image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:44.234236image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:44.818022image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:45.389233image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:46.092866image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:46.751090image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:47.285832image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:47.823546image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:48.356432image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:49.011741image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:49.772994image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:42.336942image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:43.013328image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:43.547374image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:44.302846image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:44.866459image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:45.429181image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:46.139514image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:46.798256image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:47.332486image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:47.862497image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:48.397615image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:49.058140image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:49.816788image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:42.536547image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:43.051515image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:43.592301image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:44.342499image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:44.912669image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:45.471684image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:46.286745image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:46.839743image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:47.372263image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:47.905373image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:48.436886image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:49.103073image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:49.865215image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:42.576567image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:43.097006image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:43.643009image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:44.386965image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:44.959489image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:45.514308image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:46.332651image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:46.881053image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:47.418631image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:47.950128image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:48.481045image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:49.150429image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:49.916302image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:42.615711image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:43.136853image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:43.691143image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:44.448028image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:45.003030image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:45.563976image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:46.377137image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:46.920077image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:47.459522image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:47.990225image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:48.526310image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:49.195859image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:49.956232image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:42.650476image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:43.177580image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:43.730841image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:44.488121image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:45.046333image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:45.604635image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:46.421429image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:46.956047image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:47.498672image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:48.029037image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:48.565106image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:49.238609image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:49.998605image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:42.688130image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:43.215692image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:43.769703image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:44.527508image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:45.095441image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:45.648369image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:46.463599image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:46.991785image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:47.536286image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:48.073382image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:48.605976image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:49.280986image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:50.040987image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:42.727002image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:43.257368image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:43.810299image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:44.569156image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:45.141676image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:45.835261image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:46.506498image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:47.029653image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:47.576861image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:48.113921image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:48.647265image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:49.326671image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:50.086505image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:42.765392image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:43.298149image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:43.847610image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:44.610232image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:45.185505image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:45.875963image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:46.543199image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:47.072728image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:47.614524image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:48.150570image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:48.688583image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:49.370329image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:50.128118image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:42.803912image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:43.336609image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:43.886440image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:44.648424image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:45.228372image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:45.916476image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:46.580380image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:47.110304image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:47.652390image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:48.191031image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:48.725688image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:49.413650image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:50.182520image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:42.849819image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:43.383030image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:43.933668image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:44.694003image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:45.274583image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:45.966647image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:46.625178image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:47.157491image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:47.699695image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:48.240121image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:48.772347image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:37:49.461252image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Correlations

2025-03-12T20:37:53.036914image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Freedom to make life choicesGDP (USD)GDP per capitaGDP per capita (USD)GenerosityHealthy life expectancyIncomeGroupIndividuals using the Internet (% of population)Infant mortality rate (per 1,000 live births)Perceptions of corruptionPopulation density (people per sq. km of land area)RegionScoreSocial supportUnemployment (% of total labor force) (modeled ILO estimate)
Freedom to make life choices1.0000.3390.4030.4390.3560.4260.1620.422-0.3700.4900.1550.1600.5380.491-0.274
GDP (USD)0.3391.0000.6530.6460.0100.6050.0930.330-0.5400.2120.2150.3670.5410.487-0.057
GDP per capita0.4030.6531.0000.983-0.0070.8630.7490.911-0.8800.2810.0820.3550.8020.7430.082
GDP per capita (USD)0.4390.6460.9831.0000.0030.8880.4700.875-0.8920.2800.0700.2730.8260.7570.118
Generosity0.3560.010-0.0070.0031.0000.0140.2340.2810.0290.2910.1780.2150.1410.088-0.371
Healthy life expectancy0.4260.6050.8630.8880.0141.0000.5660.540-0.9310.2400.1970.3980.7730.7130.080
IncomeGroup0.1620.0930.7490.4700.2340.5661.0000.5380.4750.2470.1790.4380.4920.4100.201
Individuals using the Internet (% of population)0.4220.3300.9110.8750.2810.5400.5381.000-0.6520.5670.2210.5390.7370.531-0.256
Infant mortality rate (per 1,000 live births)-0.370-0.540-0.880-0.8920.029-0.9310.475-0.6521.000-0.202-0.1370.388-0.744-0.722-0.089
Perceptions of corruption0.4900.2120.2810.2800.2910.2400.2470.567-0.2021.0000.0580.1880.2800.250-0.119
Population density (people per sq. km of land area)0.1550.2150.0820.0700.1780.1970.1790.221-0.1370.0581.0000.1780.058-0.084-0.297
Region0.1600.3670.3550.2730.2150.3980.4380.5390.3880.1880.1781.0000.3580.2430.074
Score0.5380.5410.8020.8260.1410.7730.4920.737-0.7440.2800.0580.3581.0000.771-0.059
Social support0.4910.4870.7430.7570.0880.7130.4100.531-0.7220.250-0.0840.2430.7711.0000.070
Unemployment (% of total labor force) (modeled ILO estimate)-0.274-0.0570.0820.118-0.3710.0800.201-0.256-0.089-0.119-0.2970.074-0.0590.0701.000

Missing values

2025-03-12T20:37:50.253117image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-12T20:37:50.383115image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-03-12T20:37:50.483277image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Country NameScoreGDP per capitaSocial supportHealthy life expectancyFreedom to make life choicesGenerosityPerceptions of corruptionRegionIncomeGroupYearBirth rate, crude (per 1,000 people)Death rate, crude (per 1,000 people)Electric power consumption (kWh per capita)GDP (USD)GDP per capita (USD)Individuals using the Internet (% of population)Infant mortality rate (per 1,000 live births)Life expectancy at birth (years)Population density (people per sq. km of land area)Unemployment (% of total labor force) (modeled ILO estimate)
0Afghanistan40.3320.5370.2550.0850.1910.036South AsiaLow income2018NaNNaNNaN1.936300e+10520.897NaN47.9NaN56.937801.542
1Albania50.9160.8170.7900.4190.1490.032Europe & Central AsiaUpper middle income2018NaNNaNNaN1.505890e+105253.630NaN7.8NaN104.6120013.898
2Algeria50.9791.1540.6870.0770.0550.135Middle East & North AfricaUpper middle income2018NaNNaNNaN1.810000e+114278.85059.579720.1NaN17.7301012.145
3Angola40.7301.1250.2690.0000.0790.061Sub-Saharan AfricaUpper middle income2018NaNNaNNaN1.060000e+113432.390NaN51.6NaN24.713107.253
4Argentina61.0731.4680.7440.5700.0620.054Latin America & CaribbeanHigh income: nonOECD2018NaNNaNNaN5.180000e+1111652.600NaN8.8NaN16.258509.483
5Armenia40.8160.9900.6660.2600.0770.028Europe & Central AsiaLower middle income2018NaNNaNNaN1.243310e+104212.070NaN11.0NaN103.6800017.712
6Australia71.3401.5730.9100.6470.3610.302East Asia & PacificHigh income: OECD2018NaNNaNNaN1.430000e+1257305.300NaN3.1NaN3.249135.387
7Austria71.3411.5040.8910.6170.2420.224Europe & Central AsiaHigh income: OECD2018NaNNaNNaN4.560000e+1151512.90087.71052.9NaN107.207004.786
8Azerbaijan51.0241.1610.6030.4300.0310.176Europe & Central AsiaUpper middle income2018NaNNaNNaN4.693950e+104721.18079.800019.2NaN120.265005.220
9Bahrain61.3381.3660.6980.5940.2430.123Middle East & North AfricaHigh income: nonOECD2018NaNNaNNaN3.774620e+1024050.80098.64346.1NaN2017.270000.962
Country NameScoreGDP per capitaSocial supportHealthy life expectancyFreedom to make life choicesGenerosityPerceptions of corruptionRegionIncomeGroupYearBirth rate, crude (per 1,000 people)Death rate, crude (per 1,000 people)Electric power consumption (kWh per capita)GDP (USD)GDP per capita (USD)Individuals using the Internet (% of population)Infant mortality rate (per 1,000 live births)Life expectancy at birth (years)Population density (people per sq. km of land area)Unemployment (% of total labor force) (modeled ILO estimate)
126Uganda40.3221.0900.2370.4500.2590.061Sub-Saharan AfricaLow income2018NaNNaNNaN2.747690e+10643.14NaN33.8NaN213.06201.742
127Ukraine40.7931.4130.6090.1630.1870.011Europe & Central AsiaLower middle income2018NaNNaNNaN1.310000e+113095.17NaN7.5NaN77.02979.381
128United Arab Emirates72.0960.7760.6700.2840.186NaNMiddle East & North AfricaHigh income: nonOECD2018NaNNaNNaN4.140000e+1143004.9098.45006.5NaN135.60902.575
129United Kingdom71.2441.4330.8880.4640.2620.082Europe & Central AsiaHigh income: OECD2018NaNNaNNaN2.830000e+1242491.4094.89673.6NaN274.82703.954
130United States71.3981.4710.8190.5470.2910.133North AmericaHigh income: OECD2018NaNNaNNaN2.050000e+1362641.00NaN5.6NaN35.76613.933
131Uruguay61.0931.4590.7710.6250.1300.155Latin America & CaribbeanHigh income: nonOECD2018NaNNaNNaN5.959690e+1017278.00NaN6.4NaN19.70807.961
132Uzbekistan60.7191.5840.6050.7240.3280.259Europe & Central AsiaLower middle income2018NaNNaNNaN5.049990e+101532.37NaN19.1NaN77.46925.223
133Vietnam50.7151.3650.7020.6180.1770.079East Asia & PacificLower middle income2018NaNNaNNaN2.450000e+112563.8270.349616.5NaN308.12501.891
134Zambia40.5621.0470.2950.5030.2210.082Sub-Saharan AfricaLower middle income2018NaNNaNNaN2.672010e+101539.9014.300040.4NaN23.34157.209
135Zimbabwe40.3571.0940.2480.4060.1320.099Sub-Saharan AfricaLow income2018NaNNaNNaN3.100050e+102147.00NaN33.9NaN37.32464.915